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harmony

library(harmony)
Idents(ds0) <- "ds0"
Idents(ds1) <- "ds1"
Idents(ds2) <- "ds2"
CAD_merge <- merge(ds0,c(ds1,ds2))
CAD_merge$orig.ident <- Idents(CAD_merge) #存储原始分区

CAD_merge <- CAD_merge %>% 
  PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
  SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
  RunPCA()

# saveRDS(CAD_merge, "CAD_merge.rds")

CAD_merge_harmony <- RunHarmony(CAD_merge, "orig.ident", plot_convergence = T, 
                                project.dim = T, assay.use = "SCT")
CAD_merge_harmony@reductions[["pca"]] <- CAD_merge_harmony@reductions[["harmony"]]

CAD_merge_harmony <- CAD_merge_harmony %>% FindNeighbors(dims = 1:20) %>%
  RunUMAP(dims = 1:20) %>% 
  FindClusters(resolution = 0.1)

PCAPlot(CAD_merge_harmony, split.by = "orig.ident")
DimPlot(CAD_merge_harmony, reduction = "harmony", split.by = "orig.ident")

CAD_merge_harmony <- FindClusters(CAD_merge_harmony, resolution = 0.2)
umapplot(CAD_merge_harmony, split.by = "orig.ident")
# saveRDS(CAD_merge_harmony,"CAD_merge_harmony.rds")

亚群标记

selected_features <-intersect(FindVariableFeatures(ds1, nfeatures = 200)@assays[["SCT"]]@var.features,
                              FindVariableFeatures(ds2, nfeatures = 200)@assays[["SCT"]]@var.features) %>%
  intersect(FindVariableFeatures(ds0, nfeatures = 200)@assays[["SCT"]]@var.features)

## ds2作为ref
Idents(ds2) <- ds2$seurat_clusters
bst_model <- XGBoost_train_from_seuobj(ds2)
ds1 <- XGBoost_predict_from_seuobj(ds1, bst_model)
ds1 <- project2ref_celltype(ds1, ds2)
ds0 <- XGBoost_predict_from_seuobj(ds0, bst_model)
ds0 <- project2ref_celltype(ds0, ds2)

ref_sce <- mkref_scmap_from_seuobj(ds2)
ds1 <- query_scmap_from_refsce(ds1, ref_sce)
ds0 <- query_scmap_from_refsce(ds0, ref_sce)

umapplot(ds1, group.by = "ref_celltype")
umapplot(ds0, group.by = "ref_celltype")

umapplot(ds1, group.by = "scmap_idents")
umapplot(ds0, group.by = "scmap_idents")


ds1_scmap <- ds1$scmap_idents
ds1_xgb <- ds1$ref_celltype
ds0_scmap <- ds0$scmap_idents
ds0_xgb <- ds0$ref_celltype
ds2_ref <- ds2$Classification1

ds1_harmony <- subset(CAD_merge_harmony, orig.ident == "ds1")$ds2_celltype
ds0_harmony <- subset(CAD_merge_harmony, orig.ident == "ds0")$ds2_celltype

df <- data.frame(row.names = selected_features)
for(cell_type in c("SMC1","Fibromyocyte","SMC2","Pericyte")){
  df[paste0("ds2ref_",cell_type)] <- data.frame(FetchData(ds2, selected_features, cells = names(ds2_ref[ds2_ref == cell_type]))) %>% colMeans()
  
  df[paste0("ds1scmap",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_scmap[ds1_scmap == cell_type]))) %>% colMeans() 
  
  df[paste0("ds0scmap",cell_type)] <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_scmap[ds0_scmap == cell_type]))) %>% colMeans()
  
  df[paste0("ds1xgb_",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_xgb[ds1_xgb == cell_type]))) %>% colMeans()
  
  df[paste0("ds0xgb_",cell_type)] <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_xgb[ds0_xgb == cell_type]))) %>% colMeans()
  
    df[paste0("ds1harmony_",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_harmony[ds1_harmony == cell_type]))) %>% colMeans()
  
  df[paste0("ds0harmony_",cell_type)] <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_harmony[ds0_harmony == cell_type]))) %>% colMeans()
}

##只考虑SMC2

correlation heatmap

#PCA

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---
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```{r}
source("tianfengRwrappers.R")
source("XGBoost_wrapper.R")
```

## harmony
```{r}
library(harmony)
Idents(ds0) <- "ds0"
Idents(ds1) <- "ds1"
Idents(ds2) <- "ds2"
CAD_merge <- merge(ds0,c(ds1,ds2))
CAD_merge$orig.ident <- Idents(CAD_merge) #存储原始分区

CAD_merge <- CAD_merge %>% 
  PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
  SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
  RunPCA()

# saveRDS(CAD_merge, "CAD_merge.rds")

CAD_merge_harmony <- RunHarmony(CAD_merge, "orig.ident", plot_convergence = T, 
                                project.dim = T, assay.use = "SCT")
CAD_merge_harmony@reductions[["pca"]] <- CAD_merge_harmony@reductions[["harmony"]]

CAD_merge_harmony <- CAD_merge_harmony %>% FindNeighbors(dims = 1:20) %>%
  RunUMAP(dims = 1:20) %>% 
  FindClusters(resolution = 0.1)

PCAPlot(CAD_merge_harmony, split.by = "orig.ident")
DimPlot(CAD_merge_harmony, reduction = "harmony", split.by = "orig.ident")

CAD_merge_harmony <- FindClusters(CAD_merge_harmony, resolution = 0.2)
umapplot(CAD_merge_harmony, split.by = "orig.ident")
# saveRDS(CAD_merge_harmony,"CAD_merge_harmony.rds")


```

## 亚群标记
```{r}
Idents(ds2) <- ds2$Classification1
UMAPPlot(CAD_merge_harmony, label = T, cells.highlight = WhichCells(ds2, idents = "Fibroblast"))
## 5 -- SMC2  0,2 -- SMC1  4,6 -- FBM  3,8 --FB  1 --pericyte  7 -- mixed cells
levels(Idents(CAD_merge_harmony)) <- c("SMC1","Pericyte","SMC1","Fibroblast",
                               "Fibromyocyte","SMC2","Fibromyocyte","Mixed cells","Fibroblast")
CAD_merge_harmony$ds2_celltype <- Idents(CAD_merge_harmony)
f("LUM",CAD_merge_harmony,reduction = "umap")
```


```{r}
selected_features <-intersect(FindVariableFeatures(ds1, nfeatures = 200)@assays[["SCT"]]@var.features,
                              FindVariableFeatures(ds2, nfeatures = 200)@assays[["SCT"]]@var.features) %>%
  intersect(FindVariableFeatures(ds0, nfeatures = 200)@assays[["SCT"]]@var.features)

## ds2作为ref
Idents(ds2) <- ds2$seurat_clusters
bst_model <- XGBoost_train_from_seuobj(ds2)
ds1 <- XGBoost_predict_from_seuobj(ds1, bst_model)
ds1 <- project2ref_celltype(ds1, ds2)
ds0 <- XGBoost_predict_from_seuobj(ds0, bst_model)
ds0 <- project2ref_celltype(ds0, ds2)

ref_sce <- mkref_scmap_from_seuobj(ds2)
ds1 <- query_scmap_from_refsce(ds1, ref_sce)
ds0 <- query_scmap_from_refsce(ds0, ref_sce)

umapplot(ds1, group.by = "ref_celltype")
umapplot(ds0, group.by = "ref_celltype")

umapplot(ds1, group.by = "scmap_idents")
umapplot(ds0, group.by = "scmap_idents")


ds1_scmap <- ds1$scmap_idents
ds1_xgb <- ds1$ref_celltype
ds0_scmap <- ds0$scmap_idents
ds0_xgb <- ds0$ref_celltype
ds2_ref <- ds2$Classification1

ds1_harmony <- subset(CAD_merge_harmony, orig.ident == "ds1")$ds2_celltype
ds0_harmony <- subset(CAD_merge_harmony, orig.ident == "ds0")$ds2_celltype

df <- data.frame(row.names = selected_features)
for(cell_type in c("SMC1","Fibromyocyte","SMC2","Pericyte")){
  df[paste0("ds2ref_",cell_type)] <- data.frame(FetchData(ds2, selected_features, cells = names(ds2_ref[ds2_ref == cell_type]))) %>% colMeans()
  
  df[paste0("ds1scmap",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_scmap[ds1_scmap == cell_type]))) %>% colMeans() 
  
  df[paste0("ds0scmap",cell_type)] <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_scmap[ds0_scmap == cell_type]))) %>% colMeans()
  
  df[paste0("ds1xgb_",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_xgb[ds1_xgb == cell_type]))) %>% colMeans()
  
  df[paste0("ds0xgb_",cell_type)] <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_xgb[ds0_xgb == cell_type]))) %>% colMeans()
  
    df[paste0("ds1harmony_",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_harmony[ds1_harmony == cell_type]))) %>% colMeans()
  
  df[paste0("ds0harmony_",cell_type)] <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_harmony[ds0_harmony == cell_type]))) %>% colMeans()
}
```

##只考虑SMC2
```{r}
selected_features <- read.table("SMC2") #SMC2 markers in ds2
selected_features <- as.character(selected_features$V1)


temp <- get_data_table(ds0, highvar = T, type = "data")
ds0_data <- matrix(data = 0, nrow = length(selected_features), ncol = length(colnames(temp)), 
                   byrow = FALSE, dimnames = list(selected_features,colnames(temp)))
intersect_features <- intersect(selected_features, rownames(temp))
ds0_data[intersect_features,] <- temp[intersect_features,]
rm(temp)

df <- data.frame(row.names = selected_features)
for(cell_type in c("SMC2")){
  df[paste0("ds2ref_",cell_type)] <- data.frame(FetchData(ds2, selected_features, cells = names(ds2_ref[ds2_ref == cell_type]))) %>% colMeans()
  
  df[paste0("ds1scmap_",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_scmap[ds1_scmap == cell_type]))) %>% colMeans() 
  
  df[paste0("ds0scmap_",cell_type)] <- t(ds0_data[,names(ds0_scmap[ds0_scmap == cell_type])]) %>% colMeans()
  
  df[paste0("ds1xgb_",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_xgb[ds1_xgb == cell_type]))) %>% colMeans()
  
  df[paste0("ds0xgb_",cell_type)] <-  t(ds0_data[,names(ds0_xgb[ds0_xgb == cell_type])])  %>% colMeans()
  
  df[paste0("ds1harmony_",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_harmony[ds1_harmony == cell_type]))) %>% colMeans()
  
  df[paste0("ds0harmony_",cell_type)] <- t(ds0_data[,names(ds0_harmony[ds0_harmony == cell_type])]) %>% colMeans()
  
  df[paste0("ds1svm_",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_svm[ds1_svm == cell_type]))) %>% colMeans()
  
  df[paste0("ds0svm_",cell_type)] <- t(ds0_data[,names(ds0_svm[ds0_svm == cell_type])]) %>% colMeans()
}
df <- df[,!is.na(df[1,])] #删除NA列
```

# correlation heatmap
```{r fig.width=6, fig.height=6}
corr <- cor(df,method = "spearman")
pheatmap::pheatmap(corr,breaks = unique(c(seq(0.6, 1, length = 100))),
        color = colorRampPalette(c("#1E90FF", "white", "#ff2121"))(100),
        border_color = NA, cluster_rows = F, cluster_cols = F,
        main = "corr", angle_col = 45, show_rownames = T, na_col = "grey")

# corr[is.na(corr)] <- 0 #聚类时不能有NA
pheatmap::pheatmap(corr,breaks = unique(c(seq(0.4, 1, length = 100))),
        color = colorRampPalette(c("#1E90FF", "white", "#ff2121"))(100),
        border_color = NA, cluster_rows = T, cluster_cols = T, fontsize = 15,
        main = "SMC2", angle_col = 45, show_rownames = T) 

library(ggcor)
quickcor(df, type = "upper") + geom_circle2()
```

#PCA
```{r}
# res <- data.frame(row.names = c("A","B","C"))
options(stringsAsFactors = T)
rm(temp_result)
for(cell_type in c("SMC1","Fibromyocyte","Pericyte","SMC2")){
  
  df1 <- data.frame(FetchData(ds2, selected_features, cells = names(ds2_ref[ds2_ref == cell_type])),
                    method = "ds2ref", celltype = cell_type, label = paste0(cell_type, "_ds2ref"))
  
  df2 <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_scmap[ds1_scmap == cell_type])),
                    method = "ds1scmap", celltype = cell_type, label = paste0(cell_type, "_ds1scmap"))   
  
  df3 <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_scmap[ds0_scmap == cell_type])),
                    method = "ds0scmap", celltype = cell_type, label = paste0(cell_type, "_ds0scmap"))  
  
  df4 <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_xgb[ds1_xgb == cell_type])),
                    method = "ds1xgb", celltype = cell_type, label = paste0(cell_type, "_ds1xgb"))  
  
  if(cell_type != "SMC2"){
  df5 <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_xgb[ds0_xgb == cell_type])),
                    method = "ds0xgb", celltype = cell_type, label = paste0(cell_type, "_ds0xgb"))}
  
  df6 <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_harmony[ds1_harmony == cell_type])),
                    method = "ds1harmony", celltype = cell_type, label = paste0(cell_type, "_ds1harmony"))
  
  df7 <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_harmony[ds0_harmony == cell_type])),
                    method = "ds0harmony", celltype = cell_type, label = paste0(cell_type, "_ds0harmony"))  
  if(exists("temp_result")){
  temp_result <- rbind(temp_result,df1,df2,df3,df4,df5,df6,df7)
  }else{
    temp_result <- rbind(df1,df2,df3,df4,df5,df6,df7)
  }
  

  # 
  # v_ds0 <- dd[dd$type == "ds0",c(1:5)] %>% colMeans() %>% as.matrix()
  # v_ds1 <- dd[dd$type == "ds1",c(1:5)] %>% colMeans() %>% as.matrix()
  # v_ds2 <- dd[dd$type == "ds2",c(1:5)] %>% colMeans() %>% as.matrix()
  # v <- c(norm(v_ds0-v_ds2),norm(v_ds1-v_ds2),norm(v_ds0-v_ds1))
  # res[cell_type] <- v
}
# res

PCAres <- temp_result[,c(-ncol(temp_result),-ncol(temp_result)+1,-ncol(temp_result)+2)] %>%
  FactoMineR::PCA(ncp = 5, graph = F)
dd <- cbind(PCAres[["ind"]][["coord"]], 
            data.frame(method = temp_result[,"method"], celltype = temp_result[,"celltype"]), 
            label = temp_result[,"label"])
ggplot(dd) + geom_point(aes(x = Dim.1, y = Dim.2, color = method, shape = celltype), alpha = 0.2, size = 2) + theme_classic()

mean_PCA <- lapply(levels(dd$label), function(type, dd){dd[dd$label == type,c(1:5)] %>% colMeans()},dd) %>% 
    as.data.frame(col.names = levels(dd$label)) %>% t() %>% as.data.frame()

mean_PCA$label <- rownames(mean_PCA)
temp <- t(as.data.frame(strsplit(mean_PCA$label,"_")))
colnames(temp) <- c("celltype","method")
mean_PCA <- cbind(mean_PCA, temp)

p <- ggplot(data = mean_PCA) + 
  geom_point(aes(x = Dim.1, y = Dim.2, shape = celltype, color = method), size = 4) + 
  theme(text = element_text(colour = "black", size = 16),
        panel.grid.minor.y = element_blank(),panel.background = element_rect(fill = "white"),
     panel.grid.minor.x = element_blank(),
    panel.grid=element_blank(),
          plot.title = element_text(size = 16,color="black",hjust = 0.5),
          axis.title = element_text(size = 16,color ="black"), 
          axis.text = element_text(size= 16,color = "black")) + scale_shape_manual(values = c(15:18)) + scale_color_manual(values = colors_list)
p
ggsave("PCA_plot.svg",plot = p, width = 6, height = 4, device = svg)
# ggsave("PCA_plot.png",plot = p, width = 6, height = 4, device = png)

save.image()
```

```{r}

```


Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
